The Times They Are A-Changin’: How AI is Transforming Clinical Research

Following a discussion with the PCRS Network team last week during which we discussed on study we are currently performing feasibility for that incorporates use of AI in the study conduct (pretty exciting to see!), I decided to expand on a previous article I wrote recently on AI.? AI is evolving more quickly than the industry can keep up with.

The clinical research industry has historically been slow to change, yet it has started leveraging artificial intelligence (AI) to address persistent challenges such as patient recruitment, trial complexity, and data management. As I talk with people across the industry, I find varying degrees of optimism, concern, and a few pioneers making incremental progress.? Here, I explore ways AI is currently applied and strategies for achieving widespread adoption throughout the clinical research ecosystem.

AI Applications in Clinical Research

Patient Recruitment and Retention

Patient recruitment remains a significant bottleneck in clinical trials, with many sites struggling to meet enrollment goals. AI can help by analyzing large datasets, including patient demographics, medical records, and social media activity, to identify eligible participants more accurately and efficiently. Advanced algorithms enable predictive modeling, improving site selection by identifying locations more likely to enroll patients quickly and effectively. Studies from Tufts Center for the Study of Drug Development highlight the high dropout rates in recruitment, underscoring the critical need for effective, AI-driven recruitment tools.[1]

Optimizing Contracting and Budgeting

AI can streamline contracting processes, such as Clinical Trial Agreements (CTAs) and budgeting. Standardizing contracts, including Master CTAs, reduces negotiation time by minimizing the need for repeated legal adjustments. Through AI-driven document analysis, teams can quickly identify and address variations, leading to smoother execution across sponsor networks. As noted by Applied Clinical Trials, implementing standardized agreements can reduce startup timelines by up to 30 percent, enhancing efficiency in trial startup times.[2]

Complex Protocol Design and Implementation

Clinical trial protocols are intricate, often requiring multiple amendments due to changing regulatory demands or emerging safety data. AI tools can model and anticipate the impact of protocol amendments, enabling teams to minimize disruptions. Furthermore, AI-driven simulations can assess protocol feasibility, helping “design engineers” and on-the-ground teams collaborate effectively to reduce complexity costs. Just as in engineering, balancing complexity with efficiency can help clinical research professionals streamline processes and cut costs associated with protocol adjustments.[3]

Strategies for Widespread AI Adoption in Clinical Research

Stakeholder Education and Transparency

To foster acceptance, education across stakeholders is essential. AI is often viewed with skepticism, especially concerning data privacy and regulatory compliance. Providing transparent case studies on successful AI implementations, such as improved patient recruitment or streamlined budgeting, can help bridge the knowledge gap and build trust.

Cross-Functional Team Integration

Just as aligning contract and clinical teams' objectives has shown promise in recruitment outcomes, integrating AI into cross-functional teams can ensure its outputs serve multiple stakeholder needs. Involving legal, regulatory, clinical, and data science teams in AI planning will make AI outputs more relevant and actionable across the trial lifecycle.[2]

Developing Flexible, Adaptive Systems

The dynamic nature of clinical trials, with frequent adjustments to protocols, makes flexibility essential. AI systems must be adaptable, capable of learning and refining their outputs as new data becomes available. For example, predictive models can adjust to recruitment challenges or changes in trial scope, enhancing reliability and acceptance by stakeholders who see real-time utility.

Pilot Programs with Feedback Loops

Start with small-scale AI implementations in non-critical processes to reduce risk and allow teams to experiment with technology safely. Collecting feedback from pilot users will help refine AI applications and ensure the broader organization sees tangible, incremental benefits.

Embrace the Change

Adopting AI across the clinical research ecosystem demands patience, transparency, and cross-functional collaboration. Drawing on best practices from other industries managing complex, data-intensive processes, clinical research can benefit immensely from AI, particularly in patient recruitment, contracting, and protocol design. By integrating AI into trial processes thoughtfully and incrementally, the industry can harness its full potential to accelerate trials and improve patient outcomes. Embrace it!

The title draws from Bob Dylan’s iconic song “The Times They Are A-Changin’,” a powerful anthem symbolizing transformation and progress. Much like the social shifts Dylan highlighted in 1964, AI stands to be a transformative force in clinical research, breaking long-standing barriers and paving the way for greater innovation and efficiency.

?@AIinClinicalResearch ?| ?@HealthcareInnovation ?| ??@ClinicalTrials ?| ??@ArtificialIntelligence ?| ??@MedicalResearch ?| ??@DigitalHealth ?| ??@PharmaTech ?| ??@FutureOfHealthcare ?| ??@DataDriven ?| ??@ClinicalResearch ?| ??@PatientCentric ?| ??@HealthTech ?| ??@SavingAndImprovingLives

Endnotes

[1] Tufts Center for the Study of Drug Development. Site Selection and Patient Recruitment Challenges Continue to Delay Clinical Trials. 2020.

[2] “Streamlining Clinical Trials with Master CTAs.” Applied Clinical Trials, 2021.

[3] “New Benchmarks on Protocol Amendment Practices: Trends and Their Impact on Clinical Trial Performance.” Therapeutic Innovation & Regulatory Science. Accessed July 10, 2024.